Now showing items 1-20 of 46

    • SSVM: A Amooth Support Vector Machine for Classification 

      Mangasarian, Olvi; Lee, Yuh-Jye (1999)
      Smoothing methods, extensively used for solving important math- ematical programming problems and applications, are applied here to generate and solve an unconstrained smooth reformulation of the support vector machine ...
    • Large Scale Kernel Regression via Linear Programming 

      Musicant, David; Mangasarian, Olvi (1999)
      The problem of tolerant data tting by a nonlinear surface, in- duced by a kernel-based support vector machine [24], is formulated as a linear program with fewer number of variables than that of other linear programming ...
    • Lagrangian Support Vector Machines 

      Musicant, David; Mangasarian, Olvi (2000)
      An implicit Lagrangian for the dual of a simple reformulation of the standard quadratic program of a linear support vector machine is proposed. This leads to the minimization of an unconstrained di erentiable convex ...
    • Optimization of Gamma Knife Radiosurgery 

      Shepard, David; Ferris, Michael (2000)
      The Gamma Knife is a highly specialized treatment unit that pro- vides an advanced stereotactic approach to the treatment of tumors, vascular malformations, and pain disorders within the head. Inside a shielded ...
    • FATCOP 2.0: Advanced Features in an Opportunistic Mixed Integer Programming Solver 

      Linderoth, Jeff; Ferris, Michael; Chen, Qun (2000)
      We describe FATCOP 2.0, a new parallel mixed integer program solver that works in an opportunistic computing environment provided by the Condor resource management system. We outline changes to the search strategy of ...
    • A Practical Approach to Sample-path Simulation Optimization 

      Munson, Todd; Ferris, Michael (2000)
      We propose solving continuous parametric simulation optimizations using a deterministic nonlinear optimiza- tion algorithm and sample-path simulations. The op- timization problem is written in a modeling language with ...
    • Data Selection for Support Vector Machine Classifiers 

      Olvi, Mangasarian; Fung, Glenn (2000)
      The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classi er, is formulated as a concave minimization problem and solved by a nite number ...
    • Interior Point Methods for Massive Support Vector Machines 

      Munson, Todd; Ferris, Michael (2000-05-25)
      We investigate the use of interior point methods for solving quadratic programming problems with a small number of linear constraints where the quadratic term consists of a low-rank update to a positive semi-de nite matrix. ...
    • Robust Linear and Support Vector Regression 

      Musicant, David; Mangasarian, Olvi (2000-09)
      The robust Huber M-estimator, a differentiable cost function that is quadratic for small errors and linear otherwise, is modeled exactly, in the original primal space of the problem, by an easily solvable simple convex ...
    • Semismooth Support Vector Machines 

      Munson, Todd; Ferris, Michael (2000-11-29)
      The linear support vector machine can be posed as a quadratic pro- gram in a variety of ways. In this paper, we look at a formulation using the two-norm for the misclassi cation error that leads to a positive de - nite ...
    • Slice Models in General Purpose Modeling Systems 

      Meta, Voelker; Ferris, Michael (2000-12-14)
      Slice models are collections of mathematical programs with the same structure but di erent data. Examples of slice models appear in Data Envelopment Analysis, where they are used to evaluate e ciency, and cross-validation, ...
    • Incremental Support Vector Machine Classi cation 

      Mangasarian, Olvi; Fung, Glenn (2001)
      Using a recently introduced proximal support vector ma- chine classi er [4], a very fast and simple incremental support vector machine (SVM) classi er is proposed which is capable of modifying an existing linear classi ...
    • Survival-Time Classi cation of Breast Cancer Patients 

      Wolberg, William; Mangasarian, Olvi; Lee, Yuh-Jye (2001)
      The identi cation of breast cancer patients for whom chemother- apy could prolong survival time is treated here as a data mining prob- lem. This identi cation is achieved by clustering 253 breast cancer patients into ...
    • Cross-Validation, Support Vector Machines and Slice Models 

      Voelker, Meta; Ferris, Michael (2001)
      We show how to implement the cross-validation technique used in ma- chine learning as a slice model. We describe the formulation in terms of support vector machines and extend the GAMS/DEA interface to allow for e cient ...
    • A Finite Newton Method for Classi cation Problems 

      Mangasarian, Olvi (2001)
      A fundamental classi cation problem of data mining and machine learning is that of minimizing a strongly convex, piecewise quadratic function on the n-dimensional real space Rn. We show nite termination of a Newton ...
    • Knowledge-Based Support Vector Machine Classi ers 

      Shavlik, Jude; Mangasarian, Olvi; Fung, Glenn (2001)
      Prior knowledge in the form of multiple polyhedral sets, each belonging to one of two categories, is introduced into a reformulation of a linear support vector machine classi er. The resulting formulation leads to a ...
    • Set Containment Characterization 

      Mangasarian, Olvi (2001)
      Characterization of the containment of a polyhedral set in a closed halfspace, a key factor in generating knowledge-based support vector machine classi ers [7], is extended to the following: (i) Containment of one ...
    • Data Mining via Support Vector Machines 

      Mangasarian, Olvi (2001)
      Support vector machines (SVMs) have played a key role in broad classes of problems arising in various elds. Much more recently, SVMs have become the tool of choice for problems arising in data classi - cation and mining. ...
    • SIMULATION OPTIMIZATION BASED ON A HETEROGENEOUS COMPUTING ENVIRONMENT 

      Ferris, Michael; Sinapiromsaran, Krung (2001)
      We solve a simulation optimization using a deterministic nonlinear solver based on the sample-path concept. The method used a quadratic model built from a collection of surrounding simulation points. The scheme does not ...
    • RSVM: Reduced Support Vector Machines 

      Mangasarian, Olvi; Lee, Yuh-Jye (2001-01)
      An algorithm is proposed which generates a nonlinear kernel-based separating surface that requires as little as 1% of a large dataset for its explicit evaluation. To generate this nonlinear surface, the entire dataset ...